Pedigree Rivera, Lauren A
2016., 20160322, 2016, 2016-03-22
eBook
Americans are taught to believe that upward mobility is possible for anyone who is willing to work hard, regardless of their social status, yet it is often those from affluent backgrounds who land ...the best jobs.Pedigreetakes readers behind the closed doors of top-tier investment banks, consulting firms, and law firms to reveal the truth about who really gets hired for the nation's highest-paying entry-level jobs, who doesn't, and why.
Drawing on scores of in-depth interviews as well as firsthand observation of hiring practices at some of America's most prestigious firms, Lauren Rivera shows how, at every step of the hiring process, the ways that employers define and evaluate merit are strongly skewed to favor job applicants from economically privileged backgrounds. She reveals how decision makers draw from ideas about talent-what it is, what best signals it, and who does (and does not) have it-that are deeply rooted in social class. Displaying the "right stuff" that elite employers are looking for entails considerable amounts of economic, social, and cultural resources on the part of the applicants and their parents.
Challenging our most cherished beliefs about college as a great equalizer and the job market as a level playing field,Pedigreeexposes the class biases built into American notions about the best and the brightest, and shows how social status plays a significant role in determining who reaches the top of the economic ladder.
The purpose of this study is first to explore the direct effects of team personality on team innovation implementation using different operationalizations for team‐level conscientiousness and ...emotional stability. Second, although past research offers guidance for the role of team personality in shaping team climate, only a few empirical studies have demonstrated this link. Thus, we examine how the operationalizations of the two personality characteristics at team level predict team innovation implementation via team climate for innovation. We test our model using a sample of 192 employees nested within 49 teams from different medium to large Greek organizations. Our results indicate that no effects for team mean personality are observed, but a range of effects emerge for team personality diversity. More specifically, team emotional stability diversity has a significant negative relation to team innovation implementation, whereas team conscientiousness diversity has not a direct effect on the performance criterion. However, team conscientiousness diversity is significantly related to team innovation implementation via its negative effect on team climate for innovation. Theoretical and practical implications for building innovative teams are discussed, and suggestions for future research are provided.
•Job applicants engage in moderate response distortion on HEXACO-PI-R.•Honesty-humility, extraversion, agreeableness, and conscientiousness predict lower CWB.•Honesty-humility, extraversion, ...agreeableness, and conscientiousness predict higher OCB.•Predictive validity shows small declines in applicant context.
This study examined the degree to which the predictive validity of personality declines in job applicant settings. Participants completed the 200-item HEXACO Personality Inventory-Revised, either as part of confidential research (347 non-applicants) or an actual job application (260 job applicants). Approximately 18-months later, participants completed a confidential survey measuring organizational citizenship behavior (OCB) and counterproductive work behavior (CWB). There was evidence for a small drop in predictive validity among job applicants, however honesty-humility, extraversion, agreeableness, and conscientiousness predicted lower levels of CWB and higher levels of OCB in both job applicants and non-applicants. The study also informs the use of the HEXACO model of personality in selection settings, reporting typical levels of applicant faking and facet-level predictive validity.
While many organizations' hiring practices now incorporate artificial intelligence (AI) and machine learning (ML), research suggests that job applicants may react negatively toward AI/ML-based ...selection practices. In the current research, we thus examined how organizations might mitigate adverse reactions toward AI/ML-based selection processes. In two between-subjects experiments, we recruited online samples of participants (undergraduate students and Prolific panelists, respectively) and presented them with vignettes representing various selection systems and measured participants' reactions to them. In Study 1, we manipulated (a) whether the system was managed by a human decision-maker, by AI/ML, or a combination of both (an “augmented” approach), and (b) the selection stage (screening, final stage). Results indicated that participants generally reacted more favorably toward augmented and human-based approaches, relative to AI/ML-based approaches, and further depended on participants' pre-existing familiarity levels with AI. In Study 2, we sought to replicate our findings within a specific process (selecting hotel managers) and application method (handling interview recordings). We found again that reactions toward the augmented approach generally depended on participants’ familiarity levels with AI. Our findings have implications for how (and for whom) organizations should implement AI/ML-based practices.
•People react more favorably toward AI that augments (rather than replaces) humans.•Reactions to AI are relevant to both early and late stages of the hiring process.•Augmented approaches mitigate adverse reactions to AI-based hiring for people who have low familiarity with AI.
In all modern bureaucracies, politicians retain some discretion in public employment decisions, which may lead to frictions in the selection process if political connections substitute for individual ...competence. Relying on detailed matched employer-employee data on the universe of public employees in Brazil over 1997–2014, and on a regression discontinuity design in close electoral races, we establish three main findings. First, political connections are a key and quantitatively large determinant of employment in public organizations, for both bureaucrats and frontline providers. Second, patronage is an important mechanism behind this result. Third, political considerations lead to the selection of less competent individuals.
Gamification has attracted increased attention among organizations and human resource professionals recently, as a novel and promising concept for attracting and selecting prospective employees. In ...the current study, we explore the construct validity of a new gamified assessment method in employee selection that we developed following the situational judgement test (SJT) methodology. Our findings support the applicability of game elements into a traditional form of assessment built to assess candidates' soft skills. Specifically, our study contributes to research on gamification and employee selection exploring the construct validity of a gamified assessment method indicating that the psychometric properties of SJTs and their transformation into a gamified assessment are a suitable avenue for future research and practice in this field.
Companies increasingly deploy artificial intelligence (AI) technologies in their personnel recruiting and selection process to streamline it, making it faster and more efficient. AI applications can ...be found in various stages of recruiting, such as writing job ads, screening of applicant resumes, and analyzing video interviews via face recognition software. As these new technologies significantly impact people’s lives and careers but often trigger ethical concerns, the ethicality of these AI applications needs to be comprehensively understood. However, given the novelty of AI applications in recruiting practice, the subject is still an emerging topic in academic literature. To inform and strengthen the foundation for future research, this paper systematically reviews the extant literature on the ethicality of AI-enabled recruiting to date. We identify 51 articles dealing with the topic, which we synthesize by mapping the ethical opportunities, risks, and ambiguities, as well as the proposed ways to mitigate ethical risks in practice. Based on this review, we identify gaps in the extant literature and point out moral questions that call for deeper exploration in future research.
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